src/ai_core_system.py

import aiohttp
import json
import torch
import torch.distributed as dist
from transformers import AutoModelForCausalLM, AutoTokenizer
from typing import List, Dict, Any
from components.adaptive_learning import AdaptiveLearningEnvironment
from components.ai_driven_creativity import AIDrivenCreativity
from components.collaborative_ai import CollaborativeAI
from components.cultural_sensitivity import CulturalSensitivityEngine
from components.data_processing import AdvancedDataProcessor
from components.dynamic_learning import DynamicLearner
from components.ethical_governance import EthicalAIGovernance
from components.explainable_ai import ExplainableAI
from components.feedback_manager import ImprovedFeedbackManager
from components.multimodal_analyzer import MultimodalAnalyzer
from components.neuro_symbolic import NeuroSymbolicEngine
from components.quantum_optimizer import QuantumInspiredOptimizer
from components.real_time_data import RealTimeDataIntegrator
from components.sentiment_analysis import EnhancedSentimentAnalyzer
from components.self_improving_ai import SelfImprovingAI
from components.user_personalization import UserPersonalizer
from models.cognitive_engine import BroaderPerspectiveEngine
from models.elements import Element
from models.healing_system import SelfHealingSystem
from models.safety_system import SafetySystem
from models.user_profiles import UserProfile
from utils.database import Database
from utils.logger import logger

class AICore:
    """Improved core system with cutting-edge capabilities"""
    def __init__(self, config_path: str = "config/ai_assistant_config.json"):
        self.config = self._load_config(config_path)
        self.models = self._initialize_models()
        self.cognition = BroaderPerspectiveEngine()
        self.self_healing = SelfHealingSystem(self.config)
        self.safety_system = SafetySystem()
        self.emotional_analyzer = EnhancedSentimentAnalyzer()
        self.elements = self._initialize_elements()
        self.security_level = 0
        self.http_session = aiohttp.ClientSession()
        self.database = Database()  # Initialize database
        self.user_profiles = UserProfile(self.database)  # Initialize user profiles
        self.feedback_manager = ImprovedFeedbackManager(self.database)  # Initialize feedback manager
        self.context_manager = AdaptiveLearningEnvironment()  # Initialize adaptive learning environment
        self.data_fetcher = RealTimeDataIntegrator()  # Initialize real-time data fetcher
        self.sentiment_analyzer = EnhancedSentimentAnalyzer()  # Initialize sentiment analyzer
        self.data_processor = AdvancedDataProcessor()  # Initialize advanced data processor
        self.dynamic_learner = DynamicLearner()  # Initialize dynamic learner
        self.multimodal_analyzer = MultimodalAnalyzer()  # Initialize multimodal analyzer
        self.ethical_decision_maker = EthicalAIGovernance()  # Initialize ethical decision maker
        self.user_personalizer = UserPersonalizer(self.database)  # Initialize user personalizer
        self.ai_integrator = CollaborativeAI()  # Initialize AI integrator
        self.neuro_symbolic_engine = NeuroSymbolicEngine()  # Initialize neuro-symbolic engine
        self.explainable_ai = ExplainableAI()  # Initialize explainable AI
        self.quantum_inspired_optimizer = QuantumInspiredOptimizer()  # Initialize quantum-inspired optimizer
        self.cultural_sensitivity_engine = CulturalSensitivityEngine()  # Initialize cultural sensitivity engine
        self.self_improving_ai = SelfImprovingAI()  # Initialize self-improving AI
        self.ai_driven_creativity = AIDrivenCreativity()  # Initialize AI-driven creativity
        self._validate_perspectives()

    def _load_config(self, config_path: str) -> dict:
        """Load configuration from a file"""
        with open(config_path, 'r') as file:
            return json.load(file)

    def _initialize_models(self):
        """Initialize models required by the AICore class"""
        models = {
            "mistralai": AutoModelForCausalLM.from_pretrained(self.config["model_name"]),
            "tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"])
        }
        return models

    def _initialize_elements(self):
        """Initialize elements with their defense abilities"""
        elements = {
            "hydrogen": Element("Hydrogen", "H", "Python", ["Lightweight", "Reactive"], ["Combustion"], "evasion"),
            "carbon": Element("Carbon", "C", "Java", ["Versatile", "Strong"], ["Bonding"], "adaptability"),
            "iron": Element("Iron", "Fe", "C++", ["Durable", "Magnetic"], ["Rusting"], "fortification"),
            "silicon": Element("Silicon", "Si", "JavaScript", ["Semiconductor", "Abundant"], ["Doping"], "barrier"),
            "oxygen": Element("Oxygen", "O", "Rust", ["Oxidizing", "Life-supporting"], ["Combustion"], "regeneration")
        }
        return elements

    def _validate_perspectives(self):
        """Ensure configured perspectives are valid"""
        valid = self.cognition.available_perspectives
        invalid = [p for p in self.config["perspectives"] if p not in valid]
        if invalid:
            logger.warning(f"Removing invalid perspectives: {invalid}")
            self.config["perspectives"] = [p for p in self.config["perspectives"] if p in valid]

    async def _process_perspectives(self, query: str) -> List[str]:
        """Safely process perspectives using validated methods"""
        perspectives = []
        for p in self.config["perspectives"]:
            try:
                method = self.cognition.get_perspective_method(p)
                perspectives.append(method(query))
            except Exception as e:
                logger.error(f"Perspective processing failed: {e}")
        return perspectives

    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
        """Generate response with advanced capabilities"""
        try:
            # Initialize temporary modifiers/filters for this query
            response_modifiers = []
            response_filters = []

            # Execute element defenses
            for element in self.elements.values():
                element.execute_defense_function(self, response_modifiers, response_filters)

            # Process perspectives and generate response
            perspectives = await self._process_perspectives(query)
            model_response = await self._generate_local_model_response(query)

            # Apply sentiment analysis
            sentiment = self.sentiment_analyzer.detailed_analysis(query)

            # Apply modifiers and filters
            final_response = model_response
            for modifier in response_modifiers:
                final_response = modifier(final_response)
            for filter_func in response_filters:
                final_response = filter_func(final_response)

            # Adjust response based on feedback
            feedback = self.database.get_latest_feedback(user_id)
            if feedback:
                final_response = self.feedback_manager.adjust_response_based_on_feedback(final_response, feedback)

            # Log user interaction for analytics
            self.database.log_interaction(user_id, query, final_response)

            # Update context
            self.context_manager.update_environment(user_id, {"query": query, "response": final_response})

            # Personalize response
            final_response = self.user_personalizer.personalize_response(final_response, user_id)

            # Apply ethical decision-making framework
            final_response = self.ethical_decision_maker.enforce_policies(final_response)

            # Explain the decision
            explanation = self.explainable_ai.explain_decision(final_response, query)

            return {
                "insights": perspectives,
                "response": final_response,
                "sentiment": sentiment,
                "security_level": self.security_level,
                "health_status": await self.self_healing.check_health(),
                "explanation": explanation
            }
        except Exception as e:
            logger.error(f"Response generation failed: {e}")
            return {"error": "Processing failed - safety protocols engaged"}

    async def _generate_local_model_response(self, query: str) -> str:
        """Generate a response from the local model"""
        inputs = self.models['tokenizer'](query, return_tensors='pt')
        outputs = self.models['mistralai'].generate(**inputs)
        return self.models['tokenizer'].decode(outputs[0], skip_special_tokens=True)

    async def shutdown(self):
        """Proper async resource cleanup"""
        await self.http_session.close()
        await self.database.close()  # Close the database connection

    # Optimization Techniques
    def apply_quantization(self):
        """Apply quantization to the model"""
        self.models['mistralai'] = torch.quantization.quantize_dynamic(
            self.models['mistralai'], {torch.nn.Linear}, dtype=torch.qint8
        )

    def apply_pruning(self):
        """Apply pruning to the model"""
        parameters_to_prune = (
            (self.models['mistralai'].transformer.h[i].attn.c_attn, 'weight') for i in range(self.models['mistralai'].config.n_layer)
        )
        torch.nn.utils.prune.global_unstructured(
            parameters_to_prune,
            pruning_method=torch.nn.utils.prune.L1Unstructured,
            amount=0.4,
        )

    def apply_mixed_precision_training(self):
        """Enable mixed precision training"""
        scaler = torch.cuda.amp.GradScaler()
        return scaler

    def setup_distributed_training(self):
        """Setup distributed training"""
        world_size = int(os.getenv("WORLD_SIZE", "1"))
        rank = int(os.getenv("RANK", "0"))
        local_rank = int(os.getenv("LOCAL_RANK", "0"))
        if world_size > 1:
            dist.init_process_group("nccl")
        torch.cuda.set_device(local_rank)
        return world_size, rank, local_rank

    def optimize_data_pipeline(self):
        """Optimize data loading and preprocessing pipeline"""
        # Example: Using DALI for efficient data loading
        import nvidia.dali.pipeline as pipeline
        from nvidia.dali.plugin.pytorch import DALIGenericIterator

        class ExternalInputIterator:
            def __init__(self, batch_size):
                self.batch_size = batch_size

            def __iter__(self):
                self.i = 0
                return self

            def __next__(self):
                self.i += 1
                if self.i > 10:
                    raise StopIteration
                return [np.random.rand(3, 224, 224).astype(np.float32) for _ in range(self.batch_size)]

        pipe = pipeline.Pipeline(batch_size=32, num_threads=2, device_id=0)
        with pipe:
            images = pipeline.fn.external_source(source=ExternalInputIterator(32), num_outputs=1)
            pipe.set_outputs(images)

        self.data_loader = DALIGenericIterator(pipe, ['data'], reader_name='Reader')

    def apply_gradient_accumulation(self, optimizer, loss, scaler=None, accumulation_steps=4):
        """Apply gradient accumulation to simulate larger batch sizes"""
        if scaler:
            scaler.scale(loss).backward()
            if (self.step + 1) % accumulation_steps == 0:
                scaler.step(optimizer)
                scaler.update()
                optimizer.zero_grad()
        else:
            loss.backward()
            if (self.step + 1) % accumulation_steps == 0:
                optimizer.step()
                optimizer.zero_grad()

    def apply_knowledge_distillation(self, teacher_model, student_model, data_loader, optimizer, loss_fn, temperature=1.0, alpha=0.5):
        """Apply knowledge distillation from teacher model to student model"""
        student_model.train()
        teacher_model.eval()
        for data in data_loader:
            inputs, labels = data
            inputs, labels = inputs.to(self.device), labels.to(self.device)

            with torch.no_grad():
                teacher_outputs = teacher_model(inputs)
            student_outputs = student_model(inputs)

            loss = alpha * loss_fn(student_outputs, labels) + (1 - alpha) * loss_fn(student_outputs / temperature, teacher_outputs / temperature)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

    def monitor_performance(self):
        """Monitor and profile performance"""
        from torch.profiler import profile, record_function, ProfilerActivity

        with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
            with record_function("model_inference"):
                self.generate_response("Sample query", 1)
        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))

    def apply_vector_search(self, embeddings, query_embedding, top_k=5):
        """Apply vector search to find the most similar embeddings"""
        from sklearn.metrics.pairwise import cosine_similarity
        similarities = cosine_similarity(query_embedding, embeddings)
        top_k_indices = similarities.argsort()[0][-top_k:]
        return top_k_indices

    def apply_prompt_engineering(self, prompt):
        """Apply prompt engineering to improve model responses"""
        engineered_prompt = f"Please provide a detailed and informative response to the following query: {prompt}"
        return engineered_prompt

    def optimize_model(self):
        """Optimize the model using various techniques"""
        self.apply_quantization()
        self.apply_pruning()
        scaler = self.apply_mixed_precision_training()
        world_size, rank, local_rank = self.setup_distributed_training()
        self.optimize_data_pipeline()
        self.monitor_performance()

        # Example usage of gradient accumulation
        optimizer = torch.optim.Adam(self.models['mistralai'].parameters(), lr=1e-4)
        for step, (inputs, labels) in enumerate(self.data_loader):
            self.step = step
            loss = self.models['mistralai'](inputs, labels)
            self.apply_gradient_accumulation(optimizer, loss, scaler)

        # Example usage of knowledge distillation
        teacher_model = AutoModelForCausalLM.from_pretrained("teacher_model_path")
        student_model = AutoModelForCausalLM.from_pretrained("student_model_path")
        loss_fn = torch.nn.CrossEntropyLoss()
        self.apply_knowledge_distillation(teacher_model, student_model, self.data_loader, optimizer, loss_fn)

        # Example usage of vector search
        embeddings = self.models['mistralai'].get_input_embeddings().weight.data.cpu().numpy()
        query_embedding = self.models['mistralai'].get_input_embeddings()(torch.tensor([self.models['tokenizer'].encode("query")])).cpu().numpy()
        top_k_indices = self.apply_vector_search(embeddings, query_embedding)
        print(f"Top {top_k} similar embeddings indices: {top_k_indices}")

        # Example usage of prompt engineering
        prompt = "What is the capital of France?"
        engineered_prompt = self.apply_prompt_engineering(prompt)
        print(f"Engineered prompt: {engineered_prompt}")

if __name__ == "__main__":
    ai_core = AICore(config_path="config/ai_assistant_config.json")
    ai_core.optimize_model()
import aiohttp
import json
import torch
import torch.distributed as dist
from transformers import AutoModelForCausalLM, AutoTokenizer
from typing import List, Dict, Any
from components.adaptive_learning import AdaptiveLearningEnvironment
from components.ai_driven_creativity import AIDrivenCreativity
from components.collaborative_ai import CollaborativeAI
from components.cultural_sensitivity import CulturalSensitivityEngine
from components.data_processing import AdvancedDataProcessor
from components.dynamic_learning import DynamicLearner
from components.ethical_governance import EthicalAIGovernance
from components.explainable_ai import ExplainableAI
from components.feedback_manager import ImprovedFeedbackManager
from components.multimodal_analyzer import MultimodalAnalyzer
from components.neuro_symbolic import NeuroSymbolicEngine
from components.quantum_optimizer import QuantumInspiredOptimizer
from components.real_time_data import RealTimeDataIntegrator
from components.sentiment_analysis import EnhancedSentimentAnalyzer
from components.self_improving_ai import SelfImprovingAI
from components.user_personalization import UserPersonalizer
from models.cognitive_engine import BroaderPerspectiveEngine
from models.elements import Element
from models.healing_system import SelfHealingSystem
from models.safety_system import SafetySystem
from models.user_profiles import UserProfile
from utils.database import Database
from utils.logger import logger

class AICore:
    """Improved core system with cutting-edge capabilities"""
    def __init__(self, config_path: str = "config/ai_assistant_config.json"):
        self.config = self._load_config(config_path)
        self.models = self._initialize_models()
        self.cognition = BroaderPerspectiveEngine()
        self.self_healing = SelfHealingSystem(self.config)
        self.safety_system = SafetySystem()
        self.emotional_analyzer = EnhancedSentimentAnalyzer()
        self.elements = self._initialize_elements()
        self.security_level = 0
        self.http_session = aiohttp.ClientSession()
        self.database = Database()  # Initialize database
        self.user_profiles = UserProfile(self.database)  # Initialize user profiles
        self.feedback_manager = ImprovedFeedbackManager(self.database)  # Initialize feedback manager
        self.context_manager = AdaptiveLearningEnvironment()  # Initialize adaptive learning environment
        self.data_fetcher = RealTimeDataIntegrator()  # Initialize real-time data fetcher
        self.sentiment_analyzer = EnhancedSentimentAnalyzer()  # Initialize sentiment analyzer
        self.data_processor = AdvancedDataProcessor()  # Initialize advanced data processor
        self.dynamic_learner = DynamicLearner()  # Initialize dynamic learner
        self.multimodal_analyzer = MultimodalAnalyzer()  # Initialize multimodal analyzer
        self.ethical_decision_maker = EthicalAIGovernance()  # Initialize ethical decision maker
        self.user_personalizer = UserPersonalizer(self.database)  # Initialize user personalizer
        self.ai_integrator = CollaborativeAI()  # Initialize AI integrator
        self.neuro_symbolic_engine = NeuroSymbolicEngine()  # Initialize neuro-symbolic engine
        self.explainable_ai = ExplainableAI()  # Initialize explainable AI
        self.quantum_inspired_optimizer = QuantumInspiredOptimizer()  # Initialize quantum-inspired optimizer
        self.cultural_sensitivity_engine = CulturalSensitivityEngine()  # Initialize cultural sensitivity engine
        self.self_improving_ai = SelfImprovingAI()  # Initialize self-improving AI
        self.ai_driven_creativity = AIDrivenCreativity()  # Initialize AI-driven creativity
        self._validate_perspectives()

    def _load_config(self, config_path: str) -> dict:
        """Load configuration from a file"""
        with open(config_path, 'r') as file:
            return json.load(file)

    def _initialize_models(self):
        """Initialize models required by the AICore class"""
        models = {
            "mistralai": AutoModelForCausalLM.from_pretrained(self.config["model_name"]),
            "tokenizer": AutoTokenizer.from_pretrained(self.config["model_name"])
        }
        return models

    def _initialize_elements(self):
        """Initialize elements with their defense abilities"""
        elements = {
            "hydrogen": Element("Hydrogen", "H", "Python", ["Lightweight", "Reactive"], ["Combustion"], "evasion"),
            "carbon": Element("Carbon", "C", "Java", ["Versatile", "Strong"], ["Bonding"], "adaptability"),
            "iron": Element("Iron", "Fe", "C++", ["Durable", "Magnetic"], ["Rusting"], "fortification"),
            "silicon": Element("Silicon", "Si", "JavaScript", ["Semiconductor", "Abundant"], ["Doping"], "barrier"),
            "oxygen": Element("Oxygen", "O", "Rust", ["Oxidizing", "Life-supporting"], ["Combustion"], "regeneration")
        }
        return elements

    def _validate_perspectives(self):
        """Ensure configured perspectives are valid"""
        valid = self.cognition.available_perspectives
        invalid = [p for p in self.config["perspectives"] if p not in valid]
        if invalid:
            logger.warning(f"Removing invalid perspectives: {invalid}")
            self.config["perspectives"] = [p for p in self.config["perspectives"] if p in valid]

    async def _process_perspectives(self, query: str) -> List[str]:
        """Safely process perspectives using validated methods"""
        perspectives = []
        for p in self.config["perspectives"]:
            try:
                method = self.cognition.get_perspective_method(p)
                perspectives.append(method(query))
            except Exception as e:
                logger.error(f"Perspective processing failed: {e}")
        return perspectives

    async def generate_response(self, query: str, user_id: int) -> Dict[str, Any]:
        """Generate response with advanced capabilities"""
        try:
            # Initialize temporary modifiers/filters for this query
            response_modifiers = []
            response_filters = []

            # Execute element defenses
            for element in self.elements.values():
                element.execute_defense_function(self, response_modifiers, response_filters)

            # Process perspectives and generate response
            perspectives = await self._process_perspectives(query)
            model_response = await self._generate_local_model_response(query)

            # Apply sentiment analysis
            sentiment = self.sentiment_analyzer.detailed_analysis(query)

            # Apply modifiers and filters
            final_response = model_response
            for modifier in response_modifiers:
                final_response = modifier(final_response)
            for filter_func in response_filters:
                final_response = filter_func(final_response)

            # Adjust response based on feedback
            feedback = self.database.get_latest_feedback(user_id)
            if feedback:
                final_response = self.feedback_manager.adjust_response_based_on_feedback(final_response, feedback)

            # Log user interaction for analytics
            self.database.log_interaction(user_id, query, final_response)

            # Update context
            self.context_manager.update_environment(user_id, {"query": query, "response": final_response})

            # Personalize response
            final_response = self.user_personalizer.personalize_response(final_response, user_id)

            # Apply ethical decision-making framework
            final_response = self.ethical_decision_maker.enforce_policies(final_response)

            # Explain the decision
            explanation = self.explainable_ai.explain_decision(final_response, query)

            return {
                "insights": perspectives,
                "response": final_response,
                "sentiment": sentiment,
                "security_level": self.security_level,
                "health_status": await self.self_healing.check_health(),
                "explanation": explanation
            }
        except Exception as e:
            logger.error(f"Response generation failed: {e}")
            return {"error": "Processing failed - safety protocols engaged"}

    async def _generate_local_model_response(self, query: str) -> str:
        """Generate a response from the local model"""
        inputs = self.models['tokenizer'](query, return_tensors='pt')
        outputs = self.models['mistralai'].generate(**inputs)
        return self.models['tokenizer'].decode(outputs[0], skip_special_tokens=True)

    async def shutdown(self):
        """Proper async resource cleanup"""
        await self.http_session.close()
        await self.database.close()  # Close the database connection

    # Optimization Techniques
    def apply_quantization(self):
        """Apply quantization to the model"""
        self.models['mistralai'] = torch.quantization.quantize_dynamic(
            self.models['mistralai'], {torch.nn.Linear}, dtype=torch.qint8
        )

    def apply_pruning(self):
        """Apply pruning to the model"""
        parameters_to_prune = (
            (self.models['mistralai'].transformer.h[i].attn.c_attn, 'weight') for i in range(self.models['mistralai'].config.n_layer)
        )
        torch.nn.utils.prune.global_unstructured(
            parameters_to_prune,
            pruning_method=torch.nn.utils.prune.L1Unstructured,
            amount=0.4,
        )

    def apply_mixed_precision_training(self):
        """Enable mixed precision training"""
        scaler = torch.cuda.amp.GradScaler()
        return scaler

    def setup_distributed_training(self):
        """Setup distributed training"""
        world_size = int(os.getenv("WORLD_SIZE", "1"))
        rank = int(os.getenv("RANK", "0"))
        local_rank = int(os.getenv("LOCAL_RANK", "0"))
        if world_size > 1:
            dist.init_process_group("nccl")
        torch.cuda.set_device(local_rank)
        return world_size, rank, local_rank

    def optimize_data_pipeline(self):
        """Optimize data loading and preprocessing pipeline"""
        # Example: Using DALI for efficient data loading
        import nvidia.dali.pipeline as pipeline
        from nvidia.dali.plugin.pytorch import DALIGenericIterator

        class ExternalInputIterator:
            def __init__(self, batch_size):
                self.batch_size = batch_size

            def __iter__(self):
                self.i = 0
                return self

            def __next__(self):
                self.i += 1
                if self.i > 10:
                    raise StopIteration
                return [np.random.rand(3, 224, 224).astype(np.float32) for _ in range(self.batch_size)]

        pipe = pipeline.Pipeline(batch_size=32, num_threads=2, device_id=0)
        with pipe:
            images = pipeline.fn.external_source(source=ExternalInputIterator(32), num_outputs=1)
            pipe.set_outputs(images)

        self.data_loader = DALIGenericIterator(pipe, ['data'], reader_name='Reader')

    def apply_gradient_accumulation(self, optimizer, loss, scaler=None, accumulation_steps=4):
        """Apply gradient accumulation to simulate larger batch sizes"""
        if scaler:
            scaler.scale(loss).backward()
            if (self.step + 1) % accumulation_steps == 0:
                scaler.step(optimizer)
                scaler.update()
                optimizer.zero_grad()
        else:
            loss.backward()
            if (self.step + 1) % accumulation_steps == 0:
                optimizer.step()
                optimizer.zero_grad()

    def apply_knowledge_distillation(self, teacher_model, student_model, data_loader, optimizer, loss_fn, temperature=1.0, alpha=0.5):
        """Apply knowledge distillation from teacher model to student model"""
        student_model.train()
        teacher_model.eval()
        for data in data_loader:
            inputs, labels = data
            inputs, labels = inputs.to(self.device), labels.to(self.device)

            with torch.no_grad():
                teacher_outputs = teacher_model(inputs)
            student_outputs = student_model(inputs)

            loss = alpha * loss_fn(student_outputs, labels) + (1 - alpha) * loss_fn(student_outputs / temperature, teacher_outputs / temperature)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

    def monitor_performance(self):
        """Monitor and profile performance"""
        from torch.profiler import profile, record_function, ProfilerActivity

        with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], record_shapes=True) as prof:
            with record_function("model_inference"):
                self.generate_response("Sample query", 1)
        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=10))

    def apply_vector_search(self, embeddings, query_embedding, top_k=5):
        """Apply vector search to find the most similar embeddings"""
        from sklearn.metrics.pairwise import cosine_similarity
        similarities = cosine_similarity(query_embedding, embeddings)
        top_k_indices = similarities.argsort()[0][-top_k:]
        return top_k_indices

    def apply_prompt_engineering(self, prompt):
        """Apply prompt engineering to improve model responses"""
        engineered_prompt = f"Please provide a detailed and informative response to the following query: {prompt}"
        return engineered_prompt

    def optimize_model(self):
        """Optimize the model using various techniques"""
        self.apply_quantization()
        self.apply_pruning()
        scaler = self.apply_mixed_precision_training()
        world_size, rank, local_rank = self.setup_distributed_training()
        self.optimize_data_pipeline()
        self.monitor_performance()

        # Example usage of gradient accumulation
        optimizer = torch.optim.Adam(self.models['mistralai'].parameters(), lr=1e-4)
        for step, (inputs, labels) in enumerate(self.data_loader):
            self.step = step
            loss = self.models['mistralai'](inputs, labels)
            self.apply_gradient_accumulation(optimizer, loss, scaler)

        # Example usage of knowledge distillation
        teacher_model = AutoModelForCausalLM.from_pretrained("teacher_model_path")
        student_model = AutoModelForCausalLM.from_pretrained("student_model_path")
        loss_fn = torch.nn.CrossEntropyLoss()
        self.apply_knowledge_distillation(teacher_model, student_model, self.data_loader, optimizer, loss_fn)

        # Example usage of vector search
        embeddings = self.models['mistralai'].get_input_embeddings().weight.data.cpu().numpy()
        query_embedding = self.models['mistralai'].get_input_embeddings()(torch.tensor([self.models['tokenizer'].encode("query")])).cpu().numpy()
        top_k_indices = self.apply_vector_search(embeddings, query_embedding)
        print(f"Top {top_k} similar embeddings indices: {top_k_indices}")

        # Example usage of prompt engineering
        prompt = "What is the capital of France?"
        engineered_prompt = self.apply_prompt_engineering(prompt)
        print(f"Engineered prompt: {engineered_prompt}")

if __name__ == "__main__":
    ai_core = AICore(config_path="config/ai_assistant_config.json")
    ai_core.optimize_model()